#!/bin/bash

################基础配置参数，需要模型审视修改##################
# 必选字段(必须在此处定义的参数): Network batch_size RANK_SIZE
# 网络名称，同目录名称
Network="Conformer_Ti"
# 训练batch_size
batch_size=128
# 训练使用的npu卡数
export RANK_SIZE=8
# ckpt文件路径
resume=""
# 数据集路径,修改为本地数据集路径
data_path=""


# 参数校验，data_path为必传参数，其他参数的增删由模型自身决定；此处新增参数需在上面有定义并赋值
for para in $*
do
    if [[ $para == --device_id* ]];then
        device_id=`echo ${para#*=}`
    elif [[ $para == --data_path* ]];then
        data_path=`echo ${para#*=}`
    elif [[ $para == --resume* ]];then
        resume=`echo ${para#*=}`
    fi
done

# 校验是否传入data_path,不需要修改
if [[ $data_path == "" ]];then
    echo "[Error] para \"data_path\" must be confing"
    exit 1
fi


###############指定训练脚本执行路径###############
# cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
cur_path=`pwd`
cur_path_last_dirname=${cur_path##*/}
if [ x"${cur_path_last_dirname}" == x"test" ];then
    test_path_dir=${cur_path}
    cd ..
    cur_path=`pwd`
else
    test_path_dir=${cur_path}/test
fi


#################创建日志输出目录，不需要修改#################
ASCEND_DEVICE_ID=0
if [ -d ${test_path_dir}/output/${ASCEND_DEVICE_ID} ];then
    rm -rf ${test_path_dir}/output/${ASCEND_DEVICE_ID}
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
else
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
fi

export SPACH_DATASETS=${data_path}
export PYTHONPATH=./:$PYTHONPATH

#################启动训练脚本#################
# 训练开始时间，不需要修改
start_time=$(date +%s)
# source 环境变量
# 非平台场景时source 环境变量
check_etp_flag=`env | grep etp_running_flag`
etp_flag=`echo ${check_etp_flag#*=}`
if [ x"${etp_flag}" != x"true" ];then
    source ${test_path_dir}/env_npu.sh
fi

python3 -u -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 \
    main.py \
    --eval \
    --batch-size 64 \
    --input-size 224 \
    --data-set IMNET \
    --num_workers 4 \
    --epochs 100 \
    --resume ${resume}\
    --model Conformer_tiny_patch16 \
    --data-path ${data_path} \
    --output_dir ${test_path_dir}/output/${ASCEND_DEVICE_ID} \
    > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/eval_${ASCEND_DEVICE_ID}.log 2>&1 &

wait
##################获取训练数据################
#训练结束时间，不需要修改
end_time=$(date +%s)
e2e_time=$(( $end_time - $start_time ))

#结果打印，不需要修改
echo "------------------ Final result ------------------"

#输出训练精度,需要模型审视修改
ActualAccuracy=`cat ${test_path_dir}/output/${ASCEND_DEVICE_ID}/eval_${ASCEND_DEVICE_ID}.log | grep 'Accuracy' | awk '{print $16}'`
#打印，不需要修改
echo "E2E Eval Duration sec : $e2e_time"
echo "ActualAccuracy = ${ActualAccuracy}"
#性能看护结果汇总
#训练用例信息，不需要修改
BatchSize=${batch_size}
DeviceType=`uname -m`
CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'

#关键信息打印到${CaseName}.log中，不需要修改
echo "Network = ${Network}" >  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "RankSize = ${RANK_SIZE}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "BatchSize = ${BatchSize}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "DeviceType = ${DeviceType}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CaseName = ${CaseName}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualAccuracy = ${ActualAccuracy}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "E2EEvalTime = ${e2e_time}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log

